Public Release: 9-Jan-2018
Research by Nicola Gennaioli and colleagues shows that investing in the stocks least favored by analysts yields five times more than buying the most recommended. Here’s why
Investors probably expect that following the suggestions of stock analysts would make them better off than doing the exact opposite. Nevertheless, recent research by Nicola Gennaioli and colleagues shows that the best way to gain excess-returns would be to invest in the shares least favored by analysts. They compute that, during the last thirty-five years, investing in the 10% of U.S. stocks analysts were most optimistic about would have yielded on average 3% a year. By contrast, investing in the 10% of stocks analysts were most pessimistic about would have yielded a staggering 15% a year.
Gennaioli and colleagues shed light on this puzzle with the help of cognitive sciences and, in particular, using Kahneman and Tversky’s concept of representativeness. Decision makers, according to this view, overweight the representative features of a group or a phenomenon. These are defined as the features that occur more frequently in that group than in a baseline reference group.
After observing strong earnings growth – the explanation goes – analysts think that the firm may be the next Google. “Googles” are in fact more frequent among firms experiencing strong growth, which makes them representative. The problem is that “Googles” are very rare in absolute terms. As a result, expectations become too optimistic, and future performance disappoints. A model of stock prices in which investor beliefs follow this logic can account both qualitatively and quantitatively for the beliefs of analysts and the dynamics of stock returns.
In related work, the authors show that the same model can account for booms and bust in the volume of credit and interest rate spreads.
These works are part a research project financed by the European Research Council aimed at taking robust insights from cognitive sciences and at incorporating them into economic models. Kahneman and Tversky’s concept of representativeness lies at the heart of this effort.
“In a classical example, we tend to think of Irishmen as redheads because red hair is much more frequent among Irishmen than among the rest of the world”, Prof. Gennaioli says. “Nevertheless, only 10% of Irishmen are redheads. In our work, we develop models of belief formation that embody this logic and study the implication of this important psychological force in different domains”.
Representativeness helps describe expectations and behavior in different domains, not only in financial markets. One such domain is the formation of stereotypes about social groups. In a recent experimental paper, Gennaioli and colleagues show that representativeness can explain self-confidence, and in particular the unwillingness of women to compete in traditionally male subjects, such as mathematics. A slight prevalence of exceptional male math ability in the data is enough to make math ability un-representative for women, driving their exaggerated under-confidence in this particular subject.
Pedro Bordalo, Nicola Gennaioli, Rafael La Porta, Andrei Shleifer, Diagnostic Expectations and Stock Returns, working paper.
Pedro Bordalo, Nicola Gennaioli, Andrei Shleifer, Diagnostic Expectations and Credit Cycles, forthcoming in The Journal of Finance.
Pedro Bordalo, Katherine Coffman, Nicola Gennaioli, Andrei Shleifer, Stereotypes, in The Quarterly Journal of Economics, Volume 131, Issue 4.
Pedro Bordalo, Katherine Coffman, Nicola Gennaioli, Andrei Shleifer, Beliefs about Gender, NBER Working Paper No. w22972.